The absolute calibration of HJ-1B thermal infrared channel is achieved by 7 times cross-calibration of Qinghai Lake in
2010, between the HJ-1B thermal infrared channel and MODIS 31, 32 channels. At the same time, the intercept of
calibration coefficients is calculated. First, the spatial match are completed between the HJ-1B IRS image and MODIS
31, 32 channels image. Then, the spectral response of HJ-1B IRS thermal channel and MODIS 31, 32 channels are used
for the spectral match. Based on the SST algorithms of MODIS, the MODTRAN4.0 is used to simulate the radiative
transfer results under the condition of variety surface temperature of the Qinghai Lake, the atmospheric profile,
observational geometry combined with the spectral response functions of the remote sensors. The quantitative linear
relationship can be established with the simulation results. The gain and offset of cross-calibration are 52.7788
1/(W/(m2•sr•μm)), -55.4137. The result needs further validation of longer sequence.

With the development of Web services technology, the number of service increases rapidly, and it becomes a challenge
task that how to efficiently discovery the services that exactly match the user's requirements from the large scale of services
library. Many semantic Web services discovery technologies proposed by the recent literatures only focus on the
keyword-based or primary semantic based service's matching. This paper studies the rules and rule reasoning based service
matching algorithm in the background of large scale services library. Firstly, the formal descriptions of semantic
web services and service matching is presented. The services' matching are divided into four levels: Exact, Plugin, Subsume
and Fail and their formal descriptions are also presented. Then, the service matching is regarded as rule-based reasoning
issues. A set of match rules are firstly given and the related services set is retrieved from services ontology base
through rule-based reasoning, and their matching levels are determined by distinguishing the relationships between service's
I/O and user's request I/O. Finally, the experiment based on two services sets show that the proposed services
matching strategy can easily implement the smart service discovery and obtains the high service discovery efficiency in
comparison with the traditional global traversal strategy.

3D urban area model has a variety aspect of application. The most exist methods follows a three step pipeline. In this
paper, we follow the pipeline to reconstruct the 3D model of urban area. An improved covariance analysis method is
proposed to remove the irrelevant part of LiDAR. This preprocessing method is more robust than the original method for
the low resolution LiDAR data. Also, a classification method based on the Markov Random Field on the connected
component is proposed in this paper. The experiment shows our method can reconstruct 3D model of urban area from
low resolution LiDAR data.

Drought has been a frequently happened type of disaster in China, and it has caused massive losses to people's lives.
Especially the drought happened in Shandong province in the late 2010, which was recognized as the severest in the past
five hundred years in some areas. Evaluation must be done in order to make proper rescue plans. Instead of collecting
data site by site, remote sensing is an efficient way to acquire data in a large area, which is very helpful for drought
identification. Some normal ways in remote sensing for drought analysis are explained and compared in this paper, and
then the VSWI method is chosen to evaluation the drought in Shandong province. Because of its free data policy and
wide availability, the data sets acquired by Terra-MODIS are chosen to identify the drought severity in Shandong
province. From the drought severity level images we can see that almost the whole area of Shandong province was lack
of water except the Weishan Lake and eastern coastline regions where large area of water exists. The southwest region,
including Heze and Jining, is in moderate drought condition, where it is used to be an important grain-producing area.
This drought condition will inevitably put a negative effect on its grain production. The central and southern areas were
in severe drought condition, but fortunately these areas are of hills and mountains, so the drought will only affect the
lives of residents. The northern parts, including Dezhou and Bingzhou areas, were also in severe drought condition, and
these regions are also important for grain-producing, so the severe drought disaster will lead to a sharp grain output cut.
This analysis results will not only shed light on the rescue process, but also give the government some clues on how to
maintain the grain supply safety.

The difficulty existing in synthetic aperture radar (SAR) image classification is large amounts of unpredictable and
inestimable speckle, leading to degradation of the image quality and concealing important objectives of interest. By
exploiting an efficient image features extraction technique, bag-of-visual-words (BOV) for its ability of 'midlevel'
feature representation, and a new developed non-local (NL-) means denosing method suitable for multiplicative speckle,
we present a novel and effective BOV framework for SAR image classification. Compared with the other two
representative algorithms, the experimental results show that the proposed algorithm has obtained more satisfactory and
cogent classification performance and performed more robustness to SAR speckle.

This paper presents our initial research on calibrating developed Multi-vision Oblique Photogrammetry System (MOPS).
First, 2D point control from so-called Radial Alignment Constrain (RAC) by Tsai's two-stage calibration is outlined and
its potential issues to camera calibration accuracy are discussed as well. Second, we make well study on image radial
distortion when principle point parameters are ignored and with perspective invariant of space line, one new radial
distortion correctness algorithm is proposed based on 2D line control. Third, procedure of Tsai's two-stage calibration is
modified to corporate with proposed radial distortion correctness algorithm and new steps to calibrate digital cameras are
summarized in detail. Finally, calibration test was implemented on selected digital camera Nikon P5100 by means of 2D
chessboard as reference object. The comparison of proposed approach with Tsai's two-stage calibration is also given in
this paper and valuable conclusions are conducted as well.

A new method, sparse representation based spectral clustering (SC) with Nyström method, is proposed for synthetic aperture radar (SAR) image segmentation. Different from the conventional SC, this proposed technique is developed by using the sparse coefficients which obtained by solving l1 minimization problem to construct the affinity matrix and the
Nyström method is applied to alleviate the segmentation process. The advantage of our proposed method is that we do not need to select the scaling parameter in the Gaussian kernel function artificially. We apply the proposed method, k-means and the classic spectral clustering algorithm with Nyström method to SAR image segmentation. The results show that compared with the other two methods, the proposed method can obtain much better segmentation results.

Automatic generation of DEM from LIDAR point clouds is attractive to photogrammetry community. This paper
explores the possibility of using Multi-Scale SVM technique to classify untextured Lidar data into ground points and
non-ground points so that DEM can be generated efficiently. First, irregular LIDAR point clouds are rasterized and a set
of features including local height variation, min/max slope, plane flatness/direction and laser return intensity are
generalized as well. Second, we establish Multi-Scale SVM classification levels by implementing SVM classier at
different scale-space of Lidar data and one defined conditional probabilistic model is computed to make final
classification. Finally, adaptive medium filter is implemented to smooth the isolated ground points mixed with little non-ground
points and because the removal of non-ground points left quite a lot "blank holes", we further triangulate
smoothed non-ground points to generate DEM automatically. The experimental results prove to be quite significant for
real applications.

Spectral unmixing in hyperspectral remote sensing image has been widely researched in the last two decades. N-FINDR
algorithm is one of the most classical and commonly-used endmember extraction algorithms. Nevertheless, it is a timeconsuming
task that cannot meet the time requirement of many applications. In order to make N-FINDR computationally
feasible, we consider parallel implementation of N-FINDR algorithm on hybrid multiple-core CPU and GPU parallel
platform. First, a multi-core CPU-based parallel N-FINDR algorithm is considered based on a modified N-FINDR with
two improvements. And by using the increasing programmability and parallelism of commodity GPU, a GPU-based
parallel N-FINDR is presented. Finally, by taking advantages of the capability of the aforementioned algorithms, a
hybrid multiple-core CPU and GPU parallel N-FINDR is proposed by using a virtual thread technique and an adaptive
algorithm in which the computational load can be adaptively adjusted according to the capability of CPU and GPU. In
experiment, our proposed parallel N-FINDR algorithms improved the accuracy of the original N-FINDR algorithm, and
most importantly, they greatly improved the performance of N-FINDR algorithm.

Because the terrain of mountain glacier is usually very rugged, it is hard to measure glaciers and estimated their changes
in larger area by conventional measuring method. With fast development of remote sensing technique, synthetic aperture
radar (SAR) interferometry is used for glacier monitoring with the ability of all-time and all-weather. Although
interferometric coherence is a very good index to glacier, it is difficult to distinguish glacier area from non-glacier area
when their coherence is similar. In this case, interferometric phase can play an important role to identify glacier. In this
paper, phase texture analysis method is proposed to extract glacier. 8 texture features were analyzed based on co-occurrence
matrix (COM), including mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and
correlation. Among them, variance, contrast and dissimilarity can distinguish glacier from non-glacier clearly most, so
they are chosen for RGB combination. Then the RGB combination image is classified into several land covers by
maximum likelihood classification (MLC). With post-classification processing, glacier area can be extracted accurately.
Landsat TM images validate the proposed method.

Three key techniques in registration of CBERS-02B image are introduced in this paper. Including an improved image
matching strategy aided by a voting algorithm, the voting algorithm directly estimates the transformation parameters of
image instead of finding the one to one corresponding, making the image matching much more efficient and reliable.
Beside that, the method of handling the interior deformation problem caused by the incorrect splicing of three sub CCD
arrays is introduced. At last, a virtual combination of satellite image technique is introduced, the adjacent satellite images
in the same orbit are virtually combined to form a large image which is taken as the orientation unit, purposed on
accelerating the orientation process speed and decreasing the probability of orientation failure. With the techniques
mentioned above, the two main problems about CBERS-02B image: the inaccurate ephemeris parameters and the poor
internal accuracy caused by the incorrect splicing of three sub CCD arrays are solved successfully, the absolute
positioning accuracy reaches 1:10000 mapping accuracy and the internal accuracy reaches sub-pixel after the orientation
process. It indicates that extensive usage of Chinese high resolution satellite imagery may become possible and practical.

In this paper, we address the problem of extracting the edge of power lines from aerial images, which is a critical step
for the identification of the components equipped on power lines and for the diagnosis of the broken-strands on power
lines. As for the problem, a novel idea is proposed based on the fact that the textural differentiation of Dissimilarity
distributions between power line and background in a square window can be represented by some points of local peaks
on the statistical curves such that the edge of power lines can be detected. Based on the novel idea, an algorithm named
Square Window wIth Four symmeTrical axiSes (SWIFTS) is developed. Experiments and analyses demonstrate that the
SWIFTS algorithm has better performance than other classical method in terms of extracting the edge of power lines,
preserving the bent information of the edge at the point of broken-strand.

By the advantages on its' early time, high resolution and cheap price compared with other high
resolution satellite images, CORONA image can be used for change detection studies, DEM extraction,
tectonic studies and other applications. CORONA image does not digitally record surface information
like other current remote sensing images, but through the exposure on the film. Because of the
recording form, it contains serious brightness deviation between different images. In addition, the
panoramic photography of CORONA generates the geometric distortion. In this paper the region of
Yili river in Xinjiang province was selected as the experiment area. Through the study on the
pre-process of CORONA images, it finally improves the quality of image that could satisfy the need of
GIS mapping and provide foundation for the follow-up process of image analysis and information
extraction.

The fusion of multi-source data is one of the most promising techniques for improved classification of remote sensing
images. This paper utilized the images by fusing the high-resolution optical and SAR images for land use classification.
The high-resolution optical and SAR images present the land features for many aspects and supplement information to
each other because of their different imaging modes and wavebands, so classical methods such as Hue - Intensity
-Saturation(HIS) transform based, Brovey(color normalized),Principal Component Substitution(PCS) approaches and
wavelet-based method are used in fusing process, then the fused results been evaluated through the calculation of some
quantitative index(Mean grey value, Standard error, Entropy, Definition). The wavelet-based fused image shows better
effect than others. A MLC(maximum likelihood classification) method was employed to the wavelet-based fused
image ,Classification accuracy was assessed using high-resolution aerial orthophotos. The overall accuracy for six
classes(Settlement, River, Agricultural field, Country road, Tree nurseries, Bare land) was found to be 94.36% with
kappa coefficient of 0.92.

In this paper, a simulation method is introduced to generate synthetic aperture radar (SAR) image based on ray tracing
algorithm. 3D models of buildings, which are triangulated and described with vectors, are introduced into the simulator
and then the simulated images can be generated under different viewing configuration. The simulation consists of three
steps -modeling of the scene, tracing and generation of high resolution SAR images. 3D models of man-made objects are
illuminated by a virtual antenna whose signal is simplified by rays sent to the objects and back to the sensor. Then the
intensity map of rays is gridded into the SAR images. In the end, two buildings, one with a plane roof and the other with
a gable roof, are imported into the simulator under different viewing configuration. The effects of layover, shadow and
double bounce are simulated correctly in geometrically. So the simulator can be used for some interpret complex SAR
images which are composed of buildings.

The relationship between image quality and optical Q is sensitive to the system design parameters such as modulation
transfer function (MTF), signal-to-noise ratio (SNR), and ground sampled distance (GSD). In this paper, two concepts
about Q and NIIRS for remote sensing imager quality analysis and design are presented, and each part physics mean in
model and formula are illustrated separately, the influence of Q on the image quality of remote sensing system is specific
analyzed, the system performance of Q = 1 and Q = 2 of remote sensing base on NIIRS are compared, the interaction
between remote sensing image quality and Q is obtained based on NIIRS by image simulations and comparing with each
other simulation results, the optimum design of remote sensors are mainly discussed. Finally, a method to improve the
image quality scale is posed for remote sensing systems, the image quality scale of remote sensing systems can be
improved by increasing along-scan sampling. Comparisons with preliminary results show that the interaction between
image quality and Q is very sensitive to the system design parameters although more validation experiments are needed.

Cloud is the common phenomenon in the obtaining and application of the visible-light remote sensing image, and these
images are usually degraded by the turbid medium (e.g., fog, cloud) in the atmosphere. This article researched the
defogging techniques of the single satellite remote sensing image on the basis of the dark channel prior and the imaging
model of the cloud images, and proposed a new approach to calculate the airlight in view of problems of asymmetric
thickness of cloud in the satellite remote sensing images and unreasonable estimation of the airlight in the existing
defogging algorithms. In my approach, cloud thickness information was added to effectively avoid the phenomenon of
'over-treatment' and 'under-treatment'. Finally, the feature of scientific and effective were validated by the successful
testament of respectively using medium-resolution and high-resolution satellite remote sensing data.

Lakes in arid regions of Central Asia act as essential components of regional water cycles, providing sparse but valuable
water resource for the fragile ecological environments and human lives. Lakes in Central Asia are sensitive to climate
change and human activities, and great changes have been found since 1960s. Mapping and monitoring these inland
lakes would improve our understanding of mechanism of lake dynamics and climatic impacts. ICESat/GLAS satellite
laser altimetry provides an efficient tool of continuously measuring lake levels in these poorly surveyed remote areas. An
automated mapping scheme of lake level changes is developed based on GLAS altimetry products, and the spatial and
temporal characteristics of 9 typical lakes in Central Asia are analyzed to validate the level accuracies. The results show
that ICESat/GLAS has a good performance of lake level monitoring, whose patterns of level changes are the same as
those of field observation, and the max differences between GLAS and field data is 3cm. Based on the results, it is
obvious that alpine lakes are increasing greatly in lake levels during 2003-2009 due to climate change, while open lakes
with dams and plain endorheic lakes decrease dramatically in water levels due to human activities, which reveals the
overexploitation of water resource in Central Asia.

Feathering is a most widely used method in seamless satellite image mosaicking. A simple but effective algorithm - double
regions growing (DRG) algorithm, which utilizes the shape content of images' valid regions, is proposed for generating
robust feathering-line before feathering. It works without any human intervention, and experiment on real satellite images
shows the advantages of the proposed method.

The building recognition from high resolution remote sensing images is an internationally advanced research field. But
there are still many difficult to be solved. In this paper, in order to extract and recognize the special building from the
high resolution remote sensing images, a new algorithm is presented. First, a self-adaptive Average Absolute Difference
Maximum edge enhancement algorithm is presented to enhance the edge of the building and suppress the background at
one time. Second, the locally self-adaptive segmentation algorithm is implemented to obtain the binary image. Third, the
thinning algorithm is implanted to obtain the single pixel edge of the building and a pruning algorithm is necessary in
order to reduce the computation times in the following process. The following step is obtained by the Hough
transformation to make the edge of the building into polygon. Finally, an Interior angle chain (IAC) is proposed to
recognize the building with different shape. The experimental results demonstrate that the new algorithm can extract the
special shape building quickly and accurately.

With the technological development background of "3S", the research area is Yachi demonstrate area in
Bijie city of Guizhou. For traditional methods, NDVI is always used to extract Vegetation information from
remote sensing image. Based on theory of dimidiate pixel model,the study region defined NDVISsoil and
NDVISveg,vegetation, the inversion dimidiate pixel model was established based on vegetation index(NDVI)
to extract vegetation coverage, vegetation coverage can be calculated by using the model of quantifying
vegetation fraction from normalized difference vegetation,then acquired relationships between NDVI Serial
Images and landscape pattern karst rock-desertification, finally yachi demonstrate area gainer karst rocky
desertification landscape pattern of distribution. Using this methods of karst rocky desertification landscape
pattern research has significantly improved the accuracy of classification, providing a scientific basis to
ecological restoration of karst rocky desertification and other integrated control.

In this paper, the support vector machine (SVM) algorithm was utilized to tackle the classification of high resolution
images from airborne digital sensor systems. Firstly, the original image was classified using SVM of four common types
of kernel functions, namely linear, polynomial, RBF and sigmoid function, and the SVM with RBF kernel function can
achieve the most satisfactory result. On the other hand, Getis-Ord Gi, one type of local spatial statistics, had been
calculated with varying lags from 1 to 10. When classifying Gi image with lag of 3 using SVM of the RBF kernel
function, an overall accuracy of 95.66% was achieved, which is more satisfactory than the result from the original image.
The result shows that Gi images with lags less than the variogram range can be used instead of the original multi-spectral
image to improve classification accuracy between features with similar spectral characteristics like trees and lawns, as a
result, to increase the overall classification accuracy.

Dynamic monitoring forest is critical for forest management and protection, while existing satellites
hardly meet the requirements of the temporal and spatial resolution for forest mapping. HJ-1 A/B,
recently launched by China, can form the 700m-wide multi-spectrum image with 30-m resolution of
any location every 2 days, also leading to large data to process. In this study, seven-feature approach,
utilizing Principal Components, NDVIs, and DEM, is developed to map forest effectively, promising
potential applications of HJ-1 A/B in land resources.

Aiming at limitations of existing multiresolution analysis (MRA) fusion methods, this paper proposes a new fusion
method which combines curvelet and wavelet transform. Curvelet transform processes edges better than wavelet
transform does. While wavelet transform handles smooth area better than curvelet transform does. As an image often
includes more than one feature, the proposed method is conducted on the basis of region segmentation and use Àtrous
wavelet transform (ATWT) to fuse smooth areas and fast discrete curvelet transform (FDCT) to fuse areas with edges.
Furthermore, an optimal objective function defined based on a balance between spectral preservation and spatial
resolution improvement is put forward to search optimal segmentation threshold. The optimal fusion result can be
obtained by fusion processing through the optimal segmentation threshold. Landsat TM multispectral (MS) images and
SPOT Panchromatic (Pan) image covering a region of Wuhan in Hubei province are tested to assess this proposed
method. Visual evaluation and statistics analysis are employed to assess the quality of fused images of different methods.
The proposed method demonstrates best results among methods being tested in this study. So by combining attributes of
both transforms, it is possible to get better image fusion result than by using wavelet and curvelet individually.

Traditional shadow detection methods are usually detected shadow areas by the single threshold in shadow feature map.
This leads to the detection results susceptible to affect by noise, and some special target (high-bright objects and green
vegetation etc.) susceptible to misdetection. In this paper, a shadow detection method is proposed based on pulse coupled
neural network (PCNN). The model can ignore small differences of pixels values in one area, because the network output
is not only associated with the pixel brightness but also associated with pixel spatial location. Firstly, a new shadow
feature map is build. Then PCNN model is applied to get optimal detection result with max entropy. The experimental
results showed that the proposed model performed better than the single threshold models.

Because of the influence of atmosphere, the characteristics of the sensor and the image recording system, the quality of
on-orbit obtained satellite images maybe get lower. The radiation distortion is caused by various factors, such as the
blurring of the imaging system, non-linear amplitude response, vignetting and shadows, transmission noise, atmospheric
interference, the changes of surface exposure and the radiance changes with the angle. According to the actual
characteristics of high-resolution satellite images, we presented a radiation correction method which solved the image
distortion problem. The radiation correction can provide a good basis for the image geometric correction, image fusion,
etc. By the actual obtaining data of satellite on-orbit experiments, this program solves the problems of chrominance,
mosaics, correction, drop-out lines, and noise existed in the images which are obtained by the raw data after decryption
and decompression. The radiation correction can provide a good basis for the image mapping process, image fusion, etc.

The technology of Mask image dodging based on Fourier transform is a good algorithm in removing the uneven
luminance within a single image. At present, the difference method and the ratio method are the methods in common use,
but they both have their own defects .For example, the difference method can keep the brightness uniformity of the
whole image, but it is deficient in local contrast; meanwhile the ratio method can work better in local contrast, but
sometimes it makes the dark areas of the original image too bright. In order to remove the defects of the two methods
effectively, this paper on the basis of research of the two methods proposes a balance solution. Experiments show that
the scheme not only can combine the advantages of the difference method and the ratio method, but also can avoid the
deficiencies of the two algorithms.

Here we analyzed many kinds of Net primary productivity (NPP) calculating methods and chose the Monteith model
based on the Light Utility Efficiency(LUE) to estimate the NPP value of Heilongjiang province in 2003. In this model,
the NOAA/AVHRR data, climate data and eradiation data were used to calculate the NPP with the support of GIS
technology. Then the spatial change of NPP was analyzed and the spatial map of NPP was drawn. The result showed that
the mean value of NPP in the study area is 329.2gC / (m2·a). There is a consistent trend between the spatial distribution
of NPP and temperature and rainfall. The NPP values of southeastern and central area are higher than that of western
drought area and Daxingan Mountains because of their better humidity and heat. The NPP values of different vegetation
have significant differences. They decrease in the sequence of forest, shrub, cropland and grassland. The NPP values
have significantly positive correlation with mean annual temperature, rainfall and NDVI.

In the paper, the parameters of hyperspectral data of the environment disaster reduction satellites have been introduced,
firstly. Then the pro-processing methods for hyperspectral data have been elaborated according to the characteristics of
the hyperspectral sensor of the environment disaster reduction satellites. After analysis the problems existing in the pre-processing
of hyperspectral data, the hyperspectral data have been employed to classify the land features. The
experimental evaluation shows that the performance of classifying the hyperspectral data of environment disaster
reduction satellites is excellent.

The traditional fog detection methods based on remote sensing mainly used polar-orbiting satellite data (MODIS,
AVHRR) to establish the fog detection model, but the transit time are always later and the time resolution are about one
day, so they cannot be good to meet the requirements of fog detection. FY2E (a geostationary satellite) data will be
chosen to build the day fog detection model for its high time resolution (one hour) and relatively rich spectrum. In this
paper object-oriented thinking and texture differences between fog and cloud were introduced to the fog detection model.
According to the simulation results of streamer radiative transfer model based on FY2E data, Snow Separation Index
(SSI) will be built to extract snow from fog and Cloud Separation Index (CSI) will be built to extract low clouds from
fog. A day fog detection model for FY2E data will be built based on object-oriented thinking and several characteristic
parameters. The experiments shows that the fog detection model proposed in this paper achieved good results.

Registration is an essential preprocessing procedure for extensive application of remote sensing images. As a robust
feature-based descriptor used for image registration, SIFT shows great superiority in describing graphical structure with
its invariant characteristic to illumination, rotation and scale changes. However, it is not well performed under low
contrast and weak edge conditions, which has a high appearance with remote sensing images. In this paper, a novel
remote sensing image registration is proposed to cope with issues that SIFT hardly performs an acceptable accurate
feature point matching. Frequency based and spatial based algorithms, which namely are Log Gabor wavelet
transformation and discrete probability relaxation respectively, are introduced as a compound strategy to figure out
mismatching candidates. Experiment results show that the proposed method could get lower root mean square error
(RMSE) and higher correct matching ratio (CMR) than other typical methods utilized to compare with. As proposed
compound outlier removal strategy is out of relying on transformation modal, it is considerably feasible and well
performed without RANSAC algorithm. Furthermore, this method is invariant to shift, rotation, illumination and scale
change dues to SIFT participation.

Karst rocky desertification is a significant environmental and ecological problem in Southwest China. In this paper, the
spectral information, spatial context and topography information were utilized to synthetically discriminate the Karst
rocky desertification degree, which are derived from The SPOT satellite imagery and DEM. By the back-propagation
neural network, we proposed the classification model structure and classified the rocky desertification levels in Du'an
County of Guangxi province, China. The results verified the classification model of Karst rocky desertification degree is
efficient and accurate.

A new image fusion approach based on the modeling of shearlet coefficients with normal inverse gaussian model is
proposed. The approach focus on the fusion of noisy images. Based on the statistical model and additive white gaussian
noise, an subband adaptive shrinkage function is derived by using the maximum a posteriori rule. And then, the new
scheme for shearlet-domain image fusion is proposed by incorporating the adaptive shrinkage rule into the fusion scheme.
Experimental results show the proposed method perfor very well with noisy images, outperform other conventional
methods.

An adaptive High-frequency Information Fusion Algorithm of Radar and Optical Images is proposed in this paper, in
order to improve the resolution of the radar image and reserve more radar information. Firstly, Hough Transform is
adopted in the process of low-resolution radar image and high-resolution optical image registration. The implicit linear
information is extracted from two different heterogeneous images for better result. Then NSCT transform is used for
decomposition and fusion. In different decomposition layers or in the same layer with different directions, fusion rules
are adaptive for the high-frequency information of images. The ratio values of high frequency information entropy,
variance, gradient and edge strength are calculated after NSCT decomposition. High frequency information entropy,
variance, gradient or edge strength, which has the smallest ratio value, is selected as an optimal rule for regional fusion.
High-frequency information of radar image could be better retained, at the same time the low-frequency information of
optical image also could be remained. Experimental results showed that our approach performs better than those methods
with single fusion rule.

Nowadays, with digital cameras and mass storage devices becoming increasingly affordable, each day thousands of
pictures are taken and images on the Internet are emerged at an astonishing rate. Image retrieval is a process of searching
valuable information that user demanded from huge images. However, it is hard to find satisfied results due to the well
known "semantic gap". Image classification plays an essential role in retrieval process. But traditional methods will
encounter problems when dealing with high-dimensional and large-scale image sets in applications. Here, we propose a
novel multi-manifold classification model for image retrieval. Firstly, we simplify the classification of images from high-dimensional
space into the one on low-dimensional manifolds, largely reducing the complexity of classification process.
Secondly, considering that traditional distance measures often fail to find correct visual semantics of manifolds,
especially when dealing with the images having complex data distribution, we also define two new distance measures
based on path-based clustering, and further applied to the construction of a multi-class image manifold. One experiment
was conducted on 2890 Web images. The comparison results between three methods show that the proposed method
achieves the highest classification accuracy.

According to the characteristic of multi-digital cameras system, the paper presents the concept of System's Interior
Orientation Parameters (SIOP) and verifies that the SIOP acquired from calibration in the inside field can effectively
improve the accuracy and efficiency of data processing of multi-digital cameras system. Importantly, the paper builds a
solution model of SIOP and verifies the feasibility of this solution model by experiment. Above all, the major research in
the paper gives an answer to the question that how to calibrate the multi-digital cameras system efficiently and
accurately.

Texture feature of image is one of the most important factors in the processing
of information extraction from satellite scene image. In this paper the texture feature
analysis was introduced in the processing of the classification of the objects in coastal
zone. During the texture analysis process, how to extract effectively the texture features is
the key factor. In the experiment of coastal classification, this paper introduced a method
of a set of texture features selection based on step-by-step discriminance. Texture is
described by Gray level co-occurrence matrix in this study, and there are 192 texture
features to describe the characteristics of coastal objects. With the features selection
method presented by this paper, five values were chosen as the representatives to classify
the object texture feature. By means of the neural networks the object classification mode
based on the texture features was defined and the object classifications of the southern
coast of Laizhou Bay were carried out. Results show the step-by-step discriminance not
only can decrease the dimension of the texture feature database, but also ensure and
improve the accuracy of the classification, and the classification accuracy was up to
83.4%. The neural networks mode is the most effective method to account for the
classification of the typical objects in coastal zone.

A new method of change detection in SAR images based on spectral clustering is presented in this paper. Spectral
clustering is employed to extract change information from a pair images acquired on the same geographical area at
different time. Watershed transform is applied to initially segment the big image into non-overlapped local regions,
leading to reduce the complexity. Experiments results and system analysis confirm the effectiveness of the proposed
algorithm.

In recent years, many researches are about building 3D object model in the fields of computer vision and
photogrammetry, and camera calibration becomes the key problem. For long focal length digital camera calibration,
there's its particularity. In this paper, aiming at long focal length lens, a camera calibration method based on point-line
combination with vanishing points is proposed. This approach overcame the demerit of the conventional calibration
theory with vanishing points; therefore the precision of calibration parameters became better.

Drought is one of the major environmental disasters in China. With the development of geographical information system
and remote sensing technology, the real time monitoring drought over the large areas can be achieved. In this paper, the
modified apparent thermal inertia (ATI) method was applied to monitor the drought while the traditional method was
mainly used in bare soil and sparse vegetation. Taking Hebei plain as the study area, this paper calculated the apparent
thermal inertia of all pixels firstly, and then got the 1km resolution classification result aggregated from 30m CCD data
of HJ-1 satellite. Through analyzing the fine resolution classification result, the pure pixels were mask and the apparent
thermal inertia was calculated. At last, the ATI values of all pixels were interpolated from the ATI of pure pixels via
ARCGIS software. The application of the model to the announcement from Meteorological Bureau has given consistent
results.

For most remote sense image applications, variations in solar illumination conditions, atmospheric scattering and
absorption, and detector performance need to be normalized, especially in time series analysis such as change detection.
For the purpose of radiometric correction, two levels of radiometric correction, absolute and relative, have been
developed for remote sense imagery. In this paper, we select the Fast Line-of-sight Atmospheric Analysis of Spectral
Hypercubes (FLAASH) algorithm as the Atmospheric correction method, and compare it with an automatic method for
relative radiometric normalization based on a linear scale invariance of the multivariate alteration detection (MAD)
transformation. The performances of both methods are compared using a landsat TM image pairs, the results from the
two techniques have been compared both visually and using a measure of the fit based on standard error statistic.

Radiometric difference, misregistration error and the determination of classification threshold for difference image
seriously influenced the detection accuracy of traditional pixel-level change detection algorithms, and it is difficult to get
the true changes of interest from various kinds of detected changes. Therefore, a novel change detection algorithm is
proposed to detect changes of man-made objects in remote sensing images. Large-size images are divided into
overlapping and multi-scale sub-images, and three kinds of multi-scale structural features (including interscale or
intrascale features), such as central-shift moments, gradient-magnitude features, gradient-orientation features and line-length
features are extracted by support vector machine (SVM) classification. Experimental results demonstrate the
feasibility and effectiveness of the proposed algorithm.

3D objects model plays an important role in 3D Geographic Information System (3DGIS). However, 3D object modeling
is a costly and time-consuming work. In this paper, a simple 3D modeling approach is presented. It's based on epipolar
image, and using constraints from regular geometry shape to construct a realistic 3D model interactively and fast. The
result can satisfy fast modeling and analysis requirement in some case of emergency rescue. Prototype software from
experiment shows the approach is efficient and promising.

Clustering analysis groups data objects based on information only found in the data that describes the
objects and the relationships. As it is a spatial method, more research are focused on remote sensing
application recently. This paper presents comparison of two classic cluster algorithms used in
hyperspectral remote sensing image classification and the results showed that the classification of
maximum likelihood algorithm is better than ISODATA algorithm.

The fusion method for the wide range resolution images will contribute to take the
advantage of high time-resolution of MODIS data and high spatial-resolution of TM data, which
will provide the time-series information matching the crop growth. The paper test the wavelet
transform model from wavelet basis, decomposition level and fusion rule. By evaluating the
quality of fusion images from several indexes, the paper analyzed the impact of fusion quality of
MODIS and TM images from the parameter setting of wavelet transform. According to the
comparison of many experiments, the study chose decomposition level 4, BIOR 6.8 of wavelet
basis and high-replace-low of fusion rule. The study showed that the fusion method of wavelet
transform could reserve the spectral feature of time-series information and enhance the spatial
resolution from 250 meter to 30 meter. The time-series fusing images could be applied for crop
monitoring.

This paper focuses on the topography measurement of island with satellite remote sensing. In this paper, remote
sensing stereo images from Cartosat-1 satellite are used to survey the topographic information of island. Ground
control points (GCPs) data are collected through on-site measurement. Then, some distributed GCPs are selected for
remote sensing image processing. The digital elevation information (DEM) is extracted from Cartosat-1 remote
sensing data by DEM extraction processing. And then, contour, slope and aspect are calculated based on DEM
information. Finally, some ground control points is selected to validate the accuracy of topographic information. The
results show that the accuracy of topographic information obtained 4.32m at horizontal position and 6.24m at vertical
elevation accuracy.

The recently-emerged compressive sensing (CS) theory goes against the Nyquist-Shannon (NS) sampling theory and
shows that signals can be recovered from far fewer samples than what the NS sampling theorem states. In this paper, to
solve the problems in image fusion step of the full-scene image mosaic for the multiple images acquired by a low-altitude
unmanned airship, a novel information mutual complement (IMC) model based on CS theory is proposed. IMC
model rests on a similar concept that was termed as the joint sparsity models (JSMs) in distributed compressive sensing
(DCS) theory, but the measurement matrix in our IMC model is rearranged in order for the multiple images to be
reconstructed as one combination. The experimental results of the BP and TSW-CS algorithm with our IMC model
certified the effectiveness and adaptability of this proposed approach, and demonstrated that it is possible to substantially
reduce the measurement rates of the signal ensemble with good performance in the compressive domain.

The paper introduces a remote inspecting method of surface vibrator by double concerned laser. This inspecting
method with two lasers has higher inspecting accuracy. It is designed for picking up higher précising sound vibrating
information. Sound vibrating information are researched by analyzing the obtained opti-electric information. Its
detecting precision is as high as laser' wavelength' level. It is designed on the basis of picking up sound technology
derived from Michelson interference method.

In this paper, the different methods of orientating different resolution satellite images with the existing vector maps are
introduced. To moderate high resolution satellite image, such as SPOT5 image, the vector road maps are often used for
the orientation, and the vector lines in the maps usually represent the road central lines. And to higher resolution satellite
image, such as QuickBird image, the vector lines in the maps usually represent the road edges, instead. Beside that, the
different extend of details of the images makes it necessary to handle them with different methods. Because in very high
resolution satellite images a lot of disturbing image features will be extracted along with the wanted one. A voting
algorithm is employed to solve the problem, the approach is based on the previous work where an edge-based voting
strategy was studied. The voting algorithm has the advantages of globally optimal and robust to noise. It can directly
estimate the transformation parameters, meanwhile providing the potential matches of edge points, these matches can
then be used in calculating the accurate orientation parameters, and providing the chance of change detection since the
unchanged objects can be marked out with this method.

Soil moisture is the important indicator of climate, hydrology, ecology, agriculture and other parameters of the land
surface and atmospheric interface. Soil moisture plays an important role on the water and energy exchange at the land
surface/atmosphere interface. Remote sensing can provide information on large area quickly and easily, so it is
significant to do research on how to monitor soil moisture by remote sensing. This paper presents a method to assess soil
moisture status using Landsat TM data over Three Gorges area in China based on TVDI. The potential of Temperature-
Vegetation Dryness Index (TVDI) from Landsat TM data in assessing soil moisture was investigated in this region. After
retrieving land surface temperature and vegetation index a TVDI model based on the features of Ts-NDVI space is
established. And finally, soil moisture status is estimated according to TVDI. It shows that TVDI has the advantages of
stability and high accuracy to estimating the soil moisture status.

We investigate how to better use mutual information (MI) to select bands for hyperspectral image classification
with less human intervention. Mutual information effectively measures the statistical dependence between two
random variables. By modeling ground truth (e.g., a reference map) as one of the two random variables, MI can
be used to find the spectral bands that contribute most to image classification. Extending our earlier work, we
propose a sliding window model and apply mutual information to construct the estimated reference map, which
need less human intervention. Experiments on the AVIRIS 92AV3C data set show that the proposed approach
outperformed the benchmark methods, removing up to 55% of bands without significant loss of classification
accuracy, compared to the 40% from that using the reference map accompanied with the data set. Meanwhile,
its performance is found to be much robust to accuracy degradation when bands are cut off beyond 60%,
revealing a better agreement in the mutual information estimation.

Drought is one of major nature disaster in the world and China. China has a vast territory and very different spatio-temporal
distribution weather condition. Therefore, drought disasters occur frequently throughout China, which may
affect large areas and cause great economic loss every year. In this paper, geostationary meteorological remote sensing
data, FY-2C/D/E VISSR and three quantitative remotely sensed models including Cloud Parameters Method (CPM),
Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI) have been used to
dynamically monitor severe drought in southwest China from 2009 to 2010. The results have effectively revealed the
occurrence, development and disappearance of this drought event. The monitoring results can be used for the relevant
disaster management departments' decision-making works.

The situation of geological disaster prevention and control in our country is very serious. The application of remote
sensing technique in geological disaster monitoring has been more than 30 years, and which has got many successful
experiences. Remote sensing technology applications has shown enormous potential in investigation, evaluation, forecast
and warning, and relief aspects of geological hazard research, and has been more and more attention to, for remote
sensing technology is an high technology means with the ability of big range, all-weather, and dynamic monitoring the
spatio-temporal changes of disasters. In the 2008 Wenchuan earthquake disaster, remote sensing technology played a
huge role in the emergency response of the earthquake damage. But it has also exposed some problems that urgent needs
to solve, the most critical problem of which is lacking of fast and efficient methods for disaster information extraction
from remote sensing image data. This problem is also the key technical problems needs to solve and improve in the work
of remote sensing application for geological hazard emergency investigation and evaluation. An interactive interpretation
method platform based on Grab Cut segmentation was proposed in this paper, and used in interaction extraction of
geological hazard information from remote sensing image. Based on ArcGIS Engine second development environment
and in the .net framework, the interactive interpretation method utilize Grab Cut segmentation algorithm was developed.
Because of Grab Cut image segmentation algorithm exploiting the texture and edge features, using this method can
obtain ideal interpretation results and better improve interpreting efficiency, with less interactive steps.

The paper analyzes and researches the possibility and measure of photoelectric-inspect of sound frequency by phase
modulating. Analyzing the 4-Frame Phase Shifting analyzing method used in sound frequency photoelectric-inspect. It is
verified that vibrator film and the position of exploring instrument determines inspecting precision. This step directly
influences sound frequency spectrum and dynamic range. A kind of vibrator film choosing reference gist has been
brought up. This inspecting method can be used in sound information analyzing.

In this paper, a optimized algorithm to recognize and remove hazes and clouds from remotely sensed images of Landsat
MSS/TM/ETM+ over land has been proposed. This algorithm uses only the image feature to automatically recognize and
remove contamination of hazes and clouds which will prevent satellite image from assessing land surface variables.
The hazes and clouds can be detected on the base of the reflectance difference with the other regions, likes thermal
spectrum region. Based on both fourth tasseled cap parameter and a haze optimized transformation(HOT) as a measure
of haze/cloud spatial density for single Landsat MSS/TM/ETM+ image, haze and clouds can be quantitatively
recognized and removed. The performance of the proposed algorithm is demonstrated experimentally. This method can
be used for atmospheric corrections to improve landscape change detection.

A new adaptive remote sensing image fusion algorithm based on Directionlet transform is proposed in this paper.
Directionlet transform introduces multi-directional anisotropic basic function based on integer lattice which can
effectively capture the anisotropic characteristics of images. For the high-frequency multi-directional coefficients, a
contrast-based criterion is adopted and the low-pass coefficients are then selected adaptively based on a weighted
criterion. Experimental results show that the proposed remote sensing fusion algorithm outperforms the wavelet-based
fusion algorithm, both in terms of visual effect as well as quantized objective evaluation index, which proves that this
algorithm is a feasible and effective remote sensing image fusion method.